Overview: Unlike previous attempts of devising mostly deterministic feedback modules [1]}, [2]}, [3]}, we propose a Cross-Modal Variational Feedback network instilling benefits of variational autoencoder (VAE) [4]}, a powerful class of probabilistic models. Let us consider a text recognition network \(\mathbf {T}\)  that has been dissected into two parts, namely \(\mathbf {T_{A}}\)  and \(\mathbf {T_{B}}\)  where the particular position of dissection is obtained empirically as described in section REF . The prior knowledge from the resultant discrete character space is modelled using a Feedback Network \(\mathbf {F}\)  by predicting affine transformation parameters. Such parameters modulate the output activation map of \(\mathbf {T_{A}}\) , as \(\mathrm {\Psi = T_{A}(I)} \in \mathbb {R}^{\hat{H} \times \hat{W} \times \hat{T}}\) , via a designed feedback conditioning layer \(\mathrm {\Phi }\) . In other words, for iteration step t, the feedback network takes the preceding iteration's output \(\mathrm {Y^{t-1}}\)  as its input and predicts the transformation parameters \(\mathcal {P}^{t}\)  as its output. As a result it learns the mapping: \(\mathrm {\mathbf {F}: Y \rightarrow \mathcal {P}}\) , such that feedback conditioning layer \(\mathrm {\Phi }\)  modulates \(\mathrm {\Psi }\)  based on \(\mathcal {P}^{t}\) . This can be depicted as \(\mathrm {\hat{\Psi ^{t}} = \Phi (\Psi ; \mathcal {P}^{t})}\)  where \(\mathrm {\hat{\Psi ^{t}}}\)  being fed to \(\mathbf {T_{B}}\)  predicts \(\mathrm {Y^{t}}\)  with higher precision.
